Patient-specific blood glucose prediction using deep learning, considering the challenges of "small dataset" and "data imbalance"
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Updated
Oct 27, 2024 - Python
Patient-specific blood glucose prediction using deep learning, considering the challenges of "small dataset" and "data imbalance"
The official implementation of the paper "Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification."
Source code for predicting Blood Glucose Concentration
The official implementation of the paper "GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks."
GluPredKit aims to make blood glucose model training and prediction more accessible.
"Personalised Short-Term Glucose Prediction via Recurrent Self-Attention Network" in CBMS 2021
Benchmark of glucose predictive models in diabetes
Implementation of architecture for 2020 OhioT1D competition submission. Includes weights from pre-training runs with Tidepool data set. Baseline architecture is N-BEATS, modifications include RNN/shared output blocks, additional Losses. https://folk.idi.ntnu.no/kerstinb/kdh/KDH_ECAI_2020_Proceedings.pdf
Official code for PhysioCGM: A Multimodal Physiological Dataset for Non-Invasive Blood Glucose Estimation (Nature Scientific Data)
Converter from different popular CGM data export formats and datasets to a unified format suitable for ML training and inference
"Jointly Predicting Postprandial Hypoglycemia and Hyperglycemia Using Continuous Glucose Monitoring Data in Type 1 Diabetes" in EMBC 2023
Thie is the repository for preprocessing the D1NAMO dataset
Data Science Project - Predicting glucose levels with data collected by non-invasive wearable device
Reproducible ML pipeline for short-term blood glucose prediction in Type 1 Diabetes.
Machine learning web app to predict user glucose levels
Deep Learning for Continuous Glucose Monitoring Prediction — Temporal Fusion Transformer, Clarke Error Grid analysis, hypoglycemia prevention for Abbott Libre/Medtronic MiniMed
Simulation tool for modeling and analyzing the dynamics of glucose and insulin in diabetic subjects.
🧬 Predicts glucose levels using R Shiny and linear regression on health data
Estudio comparativo de ARIMAX y XGBoost para predicción de glucemia en DM1 con régimen MDI. TFG — Ingeniería de la Salud, UBU 2026.
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